


import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin



class 				_UpperCamelCase			(    __lowerCAmelCase,__lowerCAmelCase    ):

   '''simple docstring'''







   @register_to_config
   def __init__(	self	:			List[Any]							,	_lowerCamelCase	:			int = 1_2_8							,	_lowerCamelCase	:			int = 2_5_6							,	_lowerCamelCase	:			float = 2_0_0_0.0							,	_lowerCamelCase	:			int = 7_6_8							,	_lowerCamelCase	:			int = 1_2							,	_lowerCamelCase	:			int = 1_2							,	_lowerCamelCase	:			int = 6_4							,	_lowerCamelCase	:			int = 2_0_4_8							,	_lowerCamelCase	:			float = 0.1							,	):






         '''simple docstring'''



         super().__init__()

         __lowerCamelCase  :   str							    =    nn.Sequential(
             nn.Linear(lowerCamelCase_							,	d_model * 4							,	bias=lowerCamelCase_				)							,	nn.SiLU()							,	nn.Linear(d_model * 4							,	d_model * 4							,	bias=lowerCamelCase_				)							,	nn.SiLU()							,	)

         __lowerCamelCase  :   List[Any]							    =    nn.Embedding(lowerCamelCase_							,	lowerCamelCase_				)
         __lowerCamelCase  :   str							    =    False

         __lowerCamelCase  :   List[Any]							    =    nn.Linear(lowerCamelCase_							,	lowerCamelCase_							,	bias=lowerCamelCase_				)

         __lowerCamelCase  :   str							    =    nn.Dropout(p=lowerCamelCase_				)

         __lowerCamelCase  :   Dict							    =    nn.ModuleList()
         for lyr_num in range(lowerCamelCase_				):
               # FiLM conditional T5 decoder
               __lowerCamelCase  :   Optional[int]							    =    DecoderLayer(d_model=lowerCamelCase_							,	d_kv=lowerCamelCase_							,	num_heads=lowerCamelCase_							,	d_ff=lowerCamelCase_							,	dropout_rate=lowerCamelCase_				)
               self.decoders.append(lowerCamelCase_				)

         __lowerCamelCase  :   Optional[Any]							    =    TaLayerNorm(lowerCamelCase_				)

         __lowerCamelCase  :   str							    =    nn.Dropout(p=lowerCamelCase_				)
         __lowerCamelCase  :   Any							    =    nn.Linear(lowerCamelCase_							,	lowerCamelCase_							,	bias=lowerCamelCase_				)

   def 	_snake_case     (	self	:			Union[str, Any]							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			List[str]				):






         '''simple docstring'''



         __lowerCamelCase  :   Tuple							    =    torch.mul(query_input.unsqueeze(-1				)							,	key_input.unsqueeze(-2				)				)
         return mask.unsqueeze(-3				)


   def 	_snake_case     (	self	:			List[Any]							,	_lowerCamelCase	:			List[str]							,	_lowerCamelCase	:			Optional[int]							,	_lowerCamelCase	:			Union[str, Any]				):






         '''simple docstring'''



         __lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase  :   Tuple							    =    decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         __lowerCamelCase  :   List[str]							    =    get_timestep_embedding(
             decoder_noise_time * self.config.max_decoder_noise_time							,	embedding_dim=self.config.d_model							,	max_period=self.config.max_decoder_noise_time							,	).to(dtype=self.dtype				)

         __lowerCamelCase  :   Union[str, Any]							    =    self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

         assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

         __lowerCamelCase  :   List[str]							    =    decoder_input_tokens.shape[1]

         # If we want to use relative positions for audio context, we can just offset
         # this sequence by the length of encodings_and_masks.
         __lowerCamelCase  :   List[Any]							    =    torch.broadcast_to(
             torch.arange(lowerCamelCase_							,	device=decoder_input_tokens.device				)							,	(batch, seq_length)							,	)

         __lowerCamelCase  :   Optional[int]							    =    self.position_encoding(lowerCamelCase_				)

         __lowerCamelCase  :   int							    =    self.continuous_inputs_projection(lowerCamelCase_				)
         inputs += position_encodings
         __lowerCamelCase  :   str							    =    self.dropout(lowerCamelCase_				)

         # decoder: No padding present.
         __lowerCamelCase  :   Union[str, Any]							    =    torch.ones(
             decoder_input_tokens.shape[:2]							,	device=decoder_input_tokens.device							,	dtype=inputs.dtype				)

         # Translate encoding masks to encoder-decoder masks.
         __lowerCamelCase  :   Optional[Any]							    =    [(x, self.encoder_decoder_mask(lowerCamelCase_							,	lowerCamelCase_				)) for x, y in encodings_and_masks]

         # cross attend style: concat encodings
         __lowerCamelCase  :   List[Any]							    =    torch.cat([x[0] for x in encodings_and_encdec_masks]							,	dim=1				)
         __lowerCamelCase  :   Optional[int]							    =    torch.cat([x[1] for x in encodings_and_encdec_masks]							,	dim=-1				)

         for lyr in self.decoders:
               __lowerCamelCase  :   Union[str, Any]							    =    lyr(
                   lowerCamelCase_							,	conditioning_emb=lowerCamelCase_							,	encoder_hidden_states=lowerCamelCase_							,	encoder_attention_mask=lowerCamelCase_							,	)[0]

         __lowerCamelCase  :   str							    =    self.decoder_norm(lowerCamelCase_				)
         __lowerCamelCase  :   List[str]							    =    self.post_dropout(lowerCamelCase_				)

         __lowerCamelCase  :   int							    =    self.spec_out(lowerCamelCase_				)
         return spec_out



class 				_UpperCamelCase			(    nn.Module    ):

   '''simple docstring'''







   def __init__(	self	:			List[str]							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			Tuple							,	_lowerCamelCase	:			List[str]							,	_lowerCamelCase	:			List[str]							,	_lowerCamelCase	:			Tuple							,	_lowerCamelCase	:			Any=1E-6				):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase  :   List[str]							    =    nn.ModuleList()

         # cond self attention: layer 0
         self.layer.append(
             TaLayerSelfAttentionCond(d_model=lowerCamelCase_							,	d_kv=lowerCamelCase_							,	num_heads=lowerCamelCase_							,	dropout_rate=lowerCamelCase_				)				)

         # cross attention: layer 1
         self.layer.append(
             TaLayerCrossAttention(
                 d_model=lowerCamelCase_							,	d_kv=lowerCamelCase_							,	num_heads=lowerCamelCase_							,	dropout_rate=lowerCamelCase_							,	layer_norm_epsilon=lowerCamelCase_							,	)				)

         # Film Cond MLP + dropout: last layer
         self.layer.append(
             TaLayerFFCond(d_model=lowerCamelCase_							,	d_ff=lowerCamelCase_							,	dropout_rate=lowerCamelCase_							,	layer_norm_epsilon=lowerCamelCase_				)				)


   def 	_snake_case     (	self	:			List[str]							,	_lowerCamelCase	:			Tuple							,	_lowerCamelCase	:			List[Any]=None							,	_lowerCamelCase	:			int=None							,	_lowerCamelCase	:			Optional[int]=None							,	_lowerCamelCase	:			Any=None							,	_lowerCamelCase	:			Union[str, Any]=None							,	):






         '''simple docstring'''



         __lowerCamelCase  :   Tuple							    =    self.layer[0](
             lowerCamelCase_							,	conditioning_emb=lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	)

         if encoder_hidden_states is not None:
               __lowerCamelCase  :   Dict							    =    torch.where(encoder_attention_mask > 0							,	0							,	-1E10				).to(
                   encoder_hidden_states.dtype				)

               __lowerCamelCase  :   Union[str, Any]							    =    self.layer[1](
                   lowerCamelCase_							,	key_value_states=lowerCamelCase_							,	attention_mask=lowerCamelCase_							,	)

         # Apply Film Conditional Feed Forward layer
         __lowerCamelCase  :   Any							    =    self.layer[-1](lowerCamelCase_							,	lowerCamelCase_				)

         return (hidden_states,)



class 				_UpperCamelCase			(    nn.Module    ):

   '''simple docstring'''







   def __init__(	self	:			Dict							,	_lowerCamelCase	:			Any							,	_lowerCamelCase	:			Tuple							,	_lowerCamelCase	:			str							,	_lowerCamelCase	:			List[Any]				):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase  :   List[Any]							    =    TaLayerNorm(lowerCamelCase_				)
         __lowerCamelCase  :   Optional[Any]							    =    TaFiLMLayer(in_features=d_model * 4							,	out_features=lowerCamelCase_				)
         __lowerCamelCase  :   Optional[int]							    =    Attention(query_dim=lowerCamelCase_							,	heads=lowerCamelCase_							,	dim_head=lowerCamelCase_							,	out_bias=lowerCamelCase_							,	scale_qk=lowerCamelCase_				)
         __lowerCamelCase  :   Tuple							    =    nn.Dropout(lowerCamelCase_				)


   def 	_snake_case     (	self	:			int							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			Tuple=None							,	_lowerCamelCase	:			Optional[Any]=None							,	):






         '''simple docstring'''



         __lowerCamelCase  :   int							    =    self.layer_norm(lowerCamelCase_				)

         if conditioning_emb is not None:
               __lowerCamelCase  :   Tuple							    =    self.FiLMLayer(lowerCamelCase_							,	lowerCamelCase_				)

         # Self-attention block
         __lowerCamelCase  :   List[str]							    =    self.attention(lowerCamelCase_				)

         __lowerCamelCase  :   List[str]							    =    hidden_states + self.dropout(lowerCamelCase_				)

         return hidden_states



class 				_UpperCamelCase			(    nn.Module    ):

   '''simple docstring'''







   def __init__(	self	:			Dict							,	_lowerCamelCase	:			str							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			str							,	_lowerCamelCase	:			Dict				):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase  :   Optional[Any]							    =    Attention(query_dim=lowerCamelCase_							,	heads=lowerCamelCase_							,	dim_head=lowerCamelCase_							,	out_bias=lowerCamelCase_							,	scale_qk=lowerCamelCase_				)
         __lowerCamelCase  :   Any							    =    TaLayerNorm(lowerCamelCase_							,	eps=lowerCamelCase_				)
         __lowerCamelCase  :   Any							    =    nn.Dropout(lowerCamelCase_				)


   def 	_snake_case     (	self	:			Optional[Any]							,	_lowerCamelCase	:			List[str]							,	_lowerCamelCase	:			Dict=None							,	_lowerCamelCase	:			int=None							,	):






         '''simple docstring'''



         __lowerCamelCase  :   Tuple							    =    self.layer_norm(lowerCamelCase_				)
         __lowerCamelCase  :   Any							    =    self.attention(
             lowerCamelCase_							,	encoder_hidden_states=lowerCamelCase_							,	attention_mask=attention_mask.squeeze(1				)							,	)
         __lowerCamelCase  :   Union[str, Any]							    =    hidden_states + self.dropout(lowerCamelCase_				)
         return layer_output



class 				_UpperCamelCase			(    nn.Module    ):

   '''simple docstring'''







   def __init__(	self	:			Union[str, Any]							,	_lowerCamelCase	:			Any							,	_lowerCamelCase	:			str							,	_lowerCamelCase	:			Optional[int]							,	_lowerCamelCase	:			Dict				):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase  :   Any							    =    TaDenseGatedActDense(d_model=lowerCamelCase_							,	d_ff=lowerCamelCase_							,	dropout_rate=lowerCamelCase_				)
         __lowerCamelCase  :   Union[str, Any]							    =    TaFiLMLayer(in_features=d_model * 4							,	out_features=lowerCamelCase_				)
         __lowerCamelCase  :   Optional[Any]							    =    TaLayerNorm(lowerCamelCase_							,	eps=lowerCamelCase_				)
         __lowerCamelCase  :   List[str]							    =    nn.Dropout(lowerCamelCase_				)


   def 	_snake_case     (	self	:			List[Any]							,	_lowerCamelCase	:			List[Any]							,	_lowerCamelCase	:			int=None				):






         '''simple docstring'''



         __lowerCamelCase  :   int							    =    self.layer_norm(lowerCamelCase_				)
         if conditioning_emb is not None:
               __lowerCamelCase  :   Union[str, Any]							    =    self.film(lowerCamelCase_							,	lowerCamelCase_				)

         __lowerCamelCase  :   List[str]							    =    self.DenseReluDense(lowerCamelCase_				)
         __lowerCamelCase  :   List[str]							    =    hidden_states + self.dropout(lowerCamelCase_				)
         return hidden_states



class 				_UpperCamelCase			(    nn.Module    ):

   '''simple docstring'''







   def __init__(	self	:			List[Any]							,	_lowerCamelCase	:			int							,	_lowerCamelCase	:			List[Any]							,	_lowerCamelCase	:			List[Any]				):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase  :   Optional[Any]							    =    nn.Linear(lowerCamelCase_							,	lowerCamelCase_							,	bias=lowerCamelCase_				)
         __lowerCamelCase  :   Any							    =    nn.Linear(lowerCamelCase_							,	lowerCamelCase_							,	bias=lowerCamelCase_				)
         __lowerCamelCase  :   Dict							    =    nn.Linear(lowerCamelCase_							,	lowerCamelCase_							,	bias=lowerCamelCase_				)
         __lowerCamelCase  :   List[Any]							    =    nn.Dropout(lowerCamelCase_				)
         __lowerCamelCase  :   Optional[int]							    =    NewGELUActivation()


   def 	_snake_case     (	self	:			Union[str, Any]							,	_lowerCamelCase	:			Optional[int]				):






         '''simple docstring'''



         __lowerCamelCase  :   List[str]							    =    self.act(self.wi_a(lowerCamelCase_				)				)
         __lowerCamelCase  :   List[Any]							    =    self.wi_a(lowerCamelCase_				)
         __lowerCamelCase  :   Any							    =    hidden_gelu * hidden_linear
         __lowerCamelCase  :   Optional[int]							    =    self.dropout(lowerCamelCase_				)

         __lowerCamelCase  :   Optional[int]							    =    self.wo(lowerCamelCase_				)
         return hidden_states



class 				_UpperCamelCase			(    nn.Module    ):

   '''simple docstring'''







   def __init__(	self	:			Optional[int]							,	_lowerCamelCase	:			Tuple							,	_lowerCamelCase	:			Dict=1E-6				):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase  :   Any							    =    nn.Parameter(torch.ones(lowerCamelCase_				)				)
         __lowerCamelCase  :   List[str]							    =    eps


   def 	_snake_case     (	self	:			List[Any]							,	_lowerCamelCase	:			Any				):






         '''simple docstring'''



         __lowerCamelCase  :   Any							    =    hidden_states.to(torch.floataa				).pow(2				).mean(-1							,	keepdim=lowerCamelCase_				)
         __lowerCamelCase  :   Any							    =    hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

         # convert into half-precision if necessary
         if self.weight.dtype in [torch.floataa, torch.bfloataa]:
               __lowerCamelCase  :   Union[str, Any]							    =    hidden_states.to(self.weight.dtype				)

         return self.weight * hidden_states



class 				_UpperCamelCase			(    nn.Module    ):

   '''simple docstring'''







   def 	_snake_case     (	self	:			List[Any]							,	_lowerCamelCase	:			torch.Tensor				):






         '''simple docstring'''



         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.044_715 * torch.pow(lowerCamelCase_							,	3.0				))				))



class 				_UpperCamelCase			(    nn.Module    ):

   '''simple docstring'''







   def __init__(	self	:			Optional[Any]							,	_lowerCamelCase	:			List[str]							,	_lowerCamelCase	:			str				):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase  :   Tuple							    =    nn.Linear(lowerCamelCase_							,	out_features * 2							,	bias=lowerCamelCase_				)


   def 	_snake_case     (	self	:			List[str]							,	_lowerCamelCase	:			Optional[Any]							,	_lowerCamelCase	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[Any]							    =    self.scale_bias(lowerCamelCase_				)
         __lowerCamelCase    ,			__lowerCamelCase  :   Dict							    =    torch.chunk(lowerCamelCase_							,	2							,	-1				)
         __lowerCamelCase  :   List[str]							    =    x * (1 + scale) + shift
         return x




from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
